{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "69952be4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-28T01:12:10.309617Z",
     "start_time": "2021-06-28T01:12:09.691778Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "24a36995",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-28T03:27:38.921470Z",
     "start_time": "2021-06-28T03:27:38.908418Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "G.edges=EdgeView([(2, 1), (2, 7), (2, 8), (2, 3), (7, 5), (7, 9), (8, 6), (6, 0), (6, 5), (6, 9), (0, 4), (0, 5), (9, 4)]), G.degree=DegreeView({2: 4, 1: 1, 7: 3, 8: 2, 3: 1, 6: 4, 0: 3, 5: 3, 9: 3, 4: 2}),G.nodes=NodeView((2, 1, 7, 8, 3, 6, 0, 5, 9, 4))\n",
      "['__class__', '__contains__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_adj', '_node', 'add_edge', 'add_edges_from', 'add_node', 'add_nodes_from', 'add_weighted_edges_from', 'adj', 'adjacency', 'adjlist_inner_dict_factory', 'adjlist_outer_dict_factory', 'clear', 'clear_edges', 'copy', 'degree', 'edge_attr_dict_factory', 'edge_subgraph', 'edges', 'get_edge_data', 'graph', 'graph_attr_dict_factory', 'has_edge', 'has_node', 'is_directed', 'is_multigraph', 'name', 'nbunch_iter', 'neighbors', 'node_attr_dict_factory', 'node_dict_factory', 'nodes', 'number_of_edges', 'number_of_nodes', 'order', 'remove_edge', 'remove_edges_from', 'remove_node', 'remove_nodes_from', 'size', 'subgraph', 'to_directed', 'to_directed_class', 'to_undirected', 'to_undirected_class', 'update']\n"
     ]
    }
   ],
   "source": [
    "before_sorted = [[2], [8, 3], [0, 4, 6, 8], [1, 9], [2, 7], \n",
    "                 [7, 8, 6], [2, 8, 5], [4, 5], [1, 5, 6, 2], [3]]\n",
    "after_sorted = [[0, 4, 6, 8], [1, 5, 6, 2], [7, 8, 6], [2, 8, 5], \n",
    "                [4, 5], [8, 3], [1, 9], [2, 7], [3], [2]]\n",
    "dealt_nodes = [(2, 1), (2, 7), (2, 8), (2, 3), (6, 0), (6, 5), (6, 8), (6, 9), (7, 2), (7, 5), (7, 9), (9, 4), (9, 7), (9, 6), (0, 4), (0, 6), (0, 5), (5, 6), (5, 0), (5, 7), (8, 2), (8, 6), (4, 0), (4, 9), (1, 2), (3, 2)]\n",
    "cmap = ['r', 'lightcyan', 'cyan', 'c', 'teal']\n",
    "color_codes = [4, 1, 2, 4, 1, 3, 1, 1, 1, 2]\n",
    "colors_previous_prder= [cmap[idx] for idx in color_codes]\n",
    "colors_fixed_order = []\n",
    "G = nx.Graph()\n",
    "G.add_edges_from(dealt_nodes)\n",
    "print(f\"{G.edges=}, {G.degree=},{G.nodes=}\")\n",
    "print(dir(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "490cbad3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-28T03:27:40.962721Z",
     "start_time": "2021-06-28T03:27:40.661373Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2=>teal\n",
      "1=>lightcyan\n",
      "7=>cyan\n",
      "8=>teal\n",
      "3=>lightcyan\n",
      "6=>c\n",
      "0=>lightcyan\n",
      "5=>lightcyan\n",
      "9=>lightcyan\n",
      "4=>cyan\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx, clr in zip(G.nodes, colors_previous_prder):\n",
    "    print(f\"{idx}=>{clr}\")\n",
    "plt.subplot(1, 3, 1)\n",
    "nx.draw_networkx(G, node_color=colors_previous_prder)\n",
    "plt.subplot(1, 3, 2)\n",
    "nx.draw_networkx(G, node_color=colors_previous_prder)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92c6a687",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
